Hi David,
I recall from the book numerical recipes that the Gramm Schmidt
methods works terrible, numerically speaking. They provide some
counterexamples too. It is better to use singular value decomposition,
which is included in scipy too.
bye
Nicky
2008/11/23 Nathan Bell <wnbell@gmail.com>:
> On Sun, Nov 23, 2008 at 2:14 PM, David Warde-Farley <dwf@cs.toronto.edu> wrote:
>>>> Is there any particular advantage to using scipy.linalg.qr over
>> numpy.linalg.qr? Is the former faster by virtue of Fortran?
>>>> I can't say offhand which would be better. In either case you'll want
> the "economy" QR, the # of vectors you're orthogonalizing is smaller
> than the length of the vector. This way you'll get a tall, skinny Q
> as opposed to a square Q.
>> --
> Nathan Bell wnbell@gmail.com>http://graphics.cs.uiuc.edu/~wnbell/> _______________________________________________
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